Product Data Analyst

• 4-8 years of experience in Data Analytics, BI, Marketing Analytics, or Growth Analytics. We value motivation and analytical sharpness as much as years of experience.
• Strong SQL experience, comfortable with complex queries against large datasets
• Working proficiency in Python (pandas, numby, statsmodel/scipy libraries)
• Solid grasp of A/B testing and experimentation fundamentals (significance, sample size, basic causal inference).
• Experience with BI tools - Looker, PowerBI and/or Tableau.
• Experience with cloud data warehouses (BigQuery preferred; Snowflake/AWS also valuable).
• Understanding of how a DSP/ad-tech ecosystem works - auctions, impressions, SSPs, MMPs. Background in a DSP, SSP, ad exchange, or mediation platform is equally welcome.
• Comfortable working with high-volume, high-cardinality data.
• Good communication skills in both English & Spanish.

• Experience in programmatic advertising, ad-tech, or mobile marketing specifically.
• Exposure to predictive modeling or basic ML (churn prediction, forecasting, segmentation).

Data and how it is used play a central role at Jampp and are at the heart of our product, business, operational, and financial decisions.

We're building a new deep learning platform based on shared embeddings - moving from hand-crafted model features to representations our models learn directly from raw data, shared across prediction, bidding, and pricing.

As Product Data Analyst, you'll join this initiative from day one, working shoulder-to-shoulder with our Data Science and ML teams as the analytical bridge between the models and the business.

Your job is to turn data into decisions. You'll translate open-ended questions from the team into clear insights, dig into why campaigns or models underperform and own the A/B testing process end to end, from design to interpretation. You'll build the dashboards and reporting frameworks that keep the team's main KPIs visible and actionable, and act as the main driver behind data-backed decision-making across the squad. It's a technical role at its core, but your real value is in bridging the gap between the engineering/ML side and the business side - making sure what's being built actually translates into impact.

• Design and evaluate ongoing A/B tests across models and campaigns, going beyond "did it win or lose" into why, and for which advertisers or segments.
• Deep-dive into auction-level and campaign data - large volumes, high cardinality (millions of apps, placements, creatives) - to surface patterns the team hasn't already been looking for.
• Build and maintain dashboards and reporting frameworks (Looker/Tableau) that give the team clear, actionable visibility into performance.
• Translate technical model changes into business impact: what it means for CPI, CPA, and ROAS, and where there's an opportunity to push for more advertiser budget.
• Bring proactive ideas to the table. We're not looking for passive reporting - we want insight, brainstorming, and pushback.
• Work with automated pipelines (SQL, Python) to keep monitoring and reporting scalable as the team and initiative grow.
• Partner closely with Data Scientists and ML Engineers: understand what's being built, flag data quality issues, and validate results from a business lens.